
# Import packages
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Set style & figures inline
sns.set()
%matplotlib inline

!pip install pandas
!pip install numpy
!pip install plotly
!pip install requests
!pip install beautifulsoup4

import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import requests
from bs4 import BeautifulSoup
import datetime

Requirement already satisfied: pandas in c:\users\belindam\anaconda3\lib\site-packages (1.4.4)
Requirement already satisfied: pytz>=2020.1 in c:\users\belindam\anaconda3\lib\site-packages (from pandas) (2022.1)
Requirement already satisfied: python-dateutil>=2.8.1 in c:\users\belindam\anaconda3\lib\site-packages (from pandas) (2.8.2)
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Requirement already satisfied: numpy in c:\users\belindam\anaconda3\lib\site-packages (1.21.5)
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Requirement already satisfied: tenacity>=6.2.0 in c:\users\belindam\anaconda3\lib\site-packages (from plotly) (8.0.1)
Requirement already satisfied: requests in c:\users\belindam\anaconda3\lib\site-packages (2.28.1)
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Requirement already satisfied: certifi>=2017.4.17 in c:\users\belindam\anaconda3\lib\site-packages (from requests) (2022.9.14)
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Requirement already satisfied: beautifulsoup4 in c:\users\belindam\anaconda3\lib\site-packages (4.11.1)
Requirement already satisfied: soupsieve>1.2 in c:\users\belindam\anaconda3\lib\site-packages (from beautifulsoup4) (2.3.1)


confirmed_cases_data = 'time_series_covid19_confirmed_global.csv'
death_cases_data = 'time_series_covid19_deaths_global.csv'
recovery_cases_data = 'time_series_covid19_recovered_global.csv'

# Import datasets as pandas dataframes
raw_data_confirmed = pd.read_csv(confirmed_cases_data)
raw_data_deaths = pd.read_csv(death_cases_data)
raw_data_recovered = pd.read_csv(recovery_cases_data)


raw_data_confirmed.head(10)


raw_data_confirmed.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 263 entries, 0 to 262
Data columns (total 81 columns):
 #   Column          Non-Null Count  Dtype  
---  ------          --------------  -----  
 0   Province/State  82 non-null     object 
 1   Country/Region  263 non-null    object 
 2   Lat             263 non-null    float64
 3   Long            263 non-null    float64
 4   1/22/20         263 non-null    int64  
 5   1/23/20         263 non-null    int64  
 6   1/24/20         263 non-null    int64  
 7   1/25/20         263 non-null    int64  
 8   1/26/20         263 non-null    int64  
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dtypes: float64(2), int64(77), object(2)
memory usage: 166.6+ KB


raw_data_confirmed.describe()


confirmed_country = raw_data_confirmed.melt(id_vars=['Province/State','Country/Region','Lat','Long'],
                                            value_vars=raw_data_confirmed.iloc[:,4:],
                                            var_name='Date', value_name='Total Pasien Terkonfirmasi')


confirmed_country.head(10)


confirmed_country["Date"] = pd.to_datetime(confirmed_country["Date"])
confirmed_country


confirmed_country = confirmed_country.drop(["Province/State","Lat","Long"], axis = 1)


confirmed_country.head(10)


confirmed_country = confirmed_country.groupby(['Date','Country/Region'],as_index=False).agg({'Total Pasien Terkonfirmasi': 'sum'})


confirmed_country.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 14168 entries, 0 to 14167
Data columns (total 3 columns):
 #   Column                      Non-Null Count  Dtype         
---  ------                      --------------  -----         
 0   Date                        14168 non-null  datetime64[ns]
 1   Country/Region              14168 non-null  object        
 2   Total Pasien Terkonfirmasi  14168 non-null  int64         
dtypes: datetime64[ns](1), int64(1), object(1)
memory usage: 332.2+ KB


confirmed_country = confirmed_country.pivot_table(index=['Date'], columns='Country/Region',
                     values='Total Pasien Terkonfirmasi', aggfunc='first').reset_index()


confirmed_country


pd.set_option('display.max_columns', None)
confirmed_country = confirmed_country.set_index(['Date'])
confirmed_country


pd.set_option('display.max_columns', None)
confirmed_country.describe(include='all')


print("Kasus terkecil namun paling tinggi: ", confirmed_country.min().max())
print("Nilai maximum namun yang paling rendah dari lainnya: ",confirmed_country.max().min())
print("Nilai maximum namun yang paling tinggi dari lainnya: ",confirmed_country.max().max())

Kasus terkecil namun paling tinggi:  548
Nilai maximum namun yang paling rendah dari lainnya:  1
Nilai maximum namun yang paling tinggi dari lainnya:  396223


confirmed_country.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 77 entries, 2020-01-22 to 2020-04-07
Columns: 184 entries, Afghanistan to Zimbabwe
dtypes: int64(184)
memory usage: 111.3 KB


enamnegara = confirmed_country[["France", "Spain", "China", "US", "Italy", "Australia"]]
enamnegara = pd.DataFrame(enamnegara)
enamnegara


plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
fig, axes = plt.subplots(nrows=6, ncols=1)
for i, c in enumerate(enamnegara.columns):
    enamnegara[c].plot(kind='line', ax=axes[i], title=c, xlabel="Tanggal", ylabel="Total Kasus Terkonfirmasi", legend=c, linewidth=4,fontsize=16)
plt.subplots_adjust(hspace = 1.0)
plt.gcf().set_size_inches(10, 28)


plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
enamnegara.plot(figsize=(18,10), linewidth=3, fontsize=20)
plt.title("Kasus Terkonfirmasi COVID-19 di Prancis, Spanyol, Cina, As, Itali, dan Autralia", fontsize=20)

Text(0.5, 1.0, 'Kasus Terkonfirmasi COVID-19 di Prancis, Spanyol, Cina, As, Itali, dan Autralia')


negara = {'Negara':pd.Series(["France", "Spain", "China","US", "Italy","Australia"]), 
        'Total Terkonfirmasi per 7 April 2020': pd.Series([110065,141942,82718,396223,135586,5895])}

datakonfirmasi = pd.DataFrame(negara)
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
datakonfirmasi.plot.bar(x="Negara", y="Total Terkonfirmasi per 7 April 2020",rot=0,figsize=(10,7), ylabel = "Jumlah Kasus", edgecolor='k', fontsize=15)
plt.title("Jumlah Akhir Kasus Terkonfirmasi di 6 Negara",fontsize=17)

Text(0.5, 1.0, 'Jumlah Akhir Kasus Terkonfirmasi di 6 Negara')


raw_data_deaths.head()


raw_data_deaths.tail()


raw_data_deaths.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 263 entries, 0 to 262
Data columns (total 81 columns):
 #   Column          Non-Null Count  Dtype  
---  ------          --------------  -----  
 0   Province/State  82 non-null     object 
 1   Country/Region  263 non-null    object 
 2   Lat             263 non-null    float64
 3   Long            263 non-null    float64
 4   1/22/20         263 non-null    int64  
 5   1/23/20         263 non-null    int64  
 6   1/24/20         263 non-null    int64  
 7   1/25/20         263 non-null    int64  
 8   1/26/20         263 non-null    int64  
 9   1/27/20         263 non-null    int64  
 10  1/28/20         263 non-null    int64  
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dtypes: float64(2), int64(77), object(2)
memory usage: 166.6+ KB


deaths = raw_data_deaths.melt(id_vars=['Province/State','Country/Region','Lat','Long'],
                                            value_vars=raw_data_deaths.iloc[:,4:],
                                            var_name='Date', value_name='Total Pasien Meninggal')

deaths["Date"] = pd.to_datetime(deaths["Date"])

deaths = deaths.drop(["Province/State","Lat","Long"], axis = 1)
deaths = deaths.groupby(['Date','Country/Region'],as_index=False).agg({'Total Pasien Meninggal': 'sum'})
deaths = deaths.pivot_table(index=['Date'], columns='Country/Region',
                     values='Total Pasien Meninggal', aggfunc='first').reset_index()

deaths = deaths.set_index(['Date'])


pd.set_option('display.max_columns', None)
deaths.head()


pd.set_option('display.max_columns', None)
deaths.tail()


deaths.index

DatetimeIndex(['2020-01-22', '2020-01-23', '2020-01-24', '2020-01-25',
               '2020-01-26', '2020-01-27', '2020-01-28', '2020-01-29',
               '2020-01-30', '2020-01-31', '2020-02-01', '2020-02-02',
               '2020-02-03', '2020-02-04', '2020-02-05', '2020-02-06',
               '2020-02-07', '2020-02-08', '2020-02-09', '2020-02-10',
               '2020-02-11', '2020-02-12', '2020-02-13', '2020-02-14',
               '2020-02-15', '2020-02-16', '2020-02-17', '2020-02-18',
               '2020-02-19', '2020-02-20', '2020-02-21', '2020-02-22',
               '2020-02-23', '2020-02-24', '2020-02-25', '2020-02-26',
               '2020-02-27', '2020-02-28', '2020-02-29', '2020-03-01',
               '2020-03-02', '2020-03-03', '2020-03-04', '2020-03-05',
               '2020-03-06', '2020-03-07', '2020-03-08', '2020-03-09',
               '2020-03-10', '2020-03-11', '2020-03-12', '2020-03-13',
               '2020-03-14', '2020-03-15', '2020-03-16', '2020-03-17',
               '2020-03-18', '2020-03-19', '2020-03-20', '2020-03-21',
               '2020-03-22', '2020-03-23', '2020-03-24', '2020-03-25',
               '2020-03-26', '2020-03-27', '2020-03-28', '2020-03-29',
               '2020-03-30', '2020-03-31', '2020-04-01', '2020-04-02',
               '2020-04-03', '2020-04-04', '2020-04-05', '2020-04-06',
               '2020-04-07'],
              dtype='datetime64[ns]', name='Date', freq=None)


pd.set_option('display.max_columns', None)
deaths.describe()


print("Kasus terkecil namun paling tinggi:", deaths.min().max())
print("Nilai tertinggi dan paling besar nilainya diantara yang lain: ",deaths.max().max())

Kasus terkecil namun paling tinggi: 17
Nilai tertinggi dan paling besar nilainya diantara yang lain:  17127


deaths = raw_data_deaths.melt(id_vars=['Province/State','Country/Region','Lat','Long'],
                                            value_vars=raw_data_deaths.iloc[:,4:],
                                            var_name='Date', value_name='Total Pasien Meninggal')

deaths["Date"] = pd.to_datetime(deaths["Date"])

deaths = deaths.drop(["Province/State","Lat","Long"], axis = 1)
deaths = deaths.groupby(['Date','Country/Region'],as_index=False).agg({'Total Pasien Meninggal': 'sum'})
deaths = deaths.pivot_table(index=['Date'], columns='Country/Region',
                     values='Total Pasien Meninggal', aggfunc='first').reset_index()

deaths = deaths.set_index(['Date'])
deaths


pd.set_option('display.max_columns', None)
kurangdaridualima = deaths[-1:].squeeze() >= 25
x = deaths.loc[:, kurangdaridualima]
death = pd.DataFrame(x)
death


print("Kasus Kematian terkecil namun paling tinggi:", death.min().max())
print("Kasus Kematian maximum namun paling besar nilainya: ",death.max().max())
print("Kasus Kematian maximum namun paling kecil nilainya: ",death.max().min())

Kasus Kematian terkecil namun paling tinggi: 17
Kasus Kematian maximum namun paling besar nilainya:  17127
Kasus Kematian maximum namun paling kecil nilainya:  26


#untuk mnegetahui masing-masing tanggal dimulai dan berakhir tiap negara
algeria = death['Algeria']
Algeria = pd.DataFrame(algeria)
Algeria = Algeria[Algeria['Algeria']>=25] 
Algeria


plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
Algeria = pd.merge(confirmed_country["Algeria"], death["Algeria"], left_index=True, right_index=True)
Algeria.rename(columns = {'Algeria_x':'Confirmed Cases'}, inplace = True)
Algeria.rename(columns = {'Algeria_y':'Death Cases'}, inplace = True)
Algeria["2020-03-26":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Algeria')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.gcf().set_size_inches(8, 7)

Argentina = pd.merge(confirmed_country["Argentina"], death["Argentina"], left_index=True, right_index=True)
Argentina.rename(columns = {'Argentina_x':'Confirmed Cases'}, inplace = True)
Argentina.rename(columns = {'Argentina_y':'Death Cases'}, inplace = True)
Argentina["2020-03-31":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Argentina')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Australia = pd.merge(confirmed_country["Australia"], death["Australia"], left_index=True, right_index=True)
Australia.rename(columns = {'Australia_x':'Confirmed Cases'}, inplace = True)
Australia.rename(columns = {'Australia_y':'Death Cases'}, inplace = True)
Australia["2020-04-03":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Australia')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Austria = pd.merge(confirmed_country["Austria"], death["Austria"], left_index=True, right_index=True)
Austria.rename(columns = {'Austria_x':'Confirmed Cases'}, inplace = True)
Austria.rename(columns = {'Austria_y':'Death Cases'}, inplace = True)
Austria["2020-03-24":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Austria')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Belgium = pd.merge(confirmed_country["Belgium"], death["Belgium"], left_index=True, right_index=True)
Belgium.rename(columns = {'Belgium_x':'Confirmed Cases'}, inplace = True)
Belgium.rename(columns = {'Belgium_y':'Death Cases'}, inplace = True)
Belgium["2020-03-20":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Belgium')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

bosniaandherzegovina = pd.merge(confirmed_country["Bosnia and Herzegovina"], death["Bosnia and Herzegovina"], left_index=True, right_index=True)
bosniaandherzegovina.rename(columns = {'Bosnia and Herzegovina_x':'Confirmed Cases'}, inplace = True)
bosniaandherzegovina.rename(columns = {'Bosnia and Herzegovina_y':'Death Cases'}, inplace = True)
bosniaandherzegovina["2020-04-06":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Bosnia and Herzegovina')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Brazil = pd.merge(confirmed_country["Brazil"], death["Brazil"], left_index=True, right_index=True)
Brazil.rename(columns = {'Brazil_x':'Confirmed Cases'}, inplace = True)
Brazil.rename(columns = {'Brazil_y':'Death Cases'}, inplace = True)
Brazil["2020-03-22":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Brazil')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Canada = pd.merge(confirmed_country["Canada"], death["Canada"], left_index=True, right_index=True)
Canada.rename(columns = {'Canada_x':'Confirmed Cases'}, inplace = True)
Canada.rename(columns = {'Canada_y':'Death Cases'}, inplace = True)
Canada["2020-03-23":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Canada')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)


Chile = pd.merge(confirmed_country["Chile"], death["Chile"], left_index=True, right_index=True)
Chile.rename(columns = {'Chile_x':'Confirmed Cases'}, inplace = True)
Chile.rename(columns = {'Chile_y':'Death Cases'}, inplace = True)
Chile["2020-04-04":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Chile')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

China = pd.merge(confirmed_country["China"], death["China"], left_index=True, right_index=True)
China.rename(columns = {'China_x':'Confirmed Cases'}, inplace = True)
China.rename(columns = {'China_y':'Death Cases'}, inplace = True)
China["2020-01-24":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di China')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Colombia = pd.merge(confirmed_country["Colombia"], death["Colombia"], left_index=True, right_index=True)
Colombia.rename(columns = {'Colombia_x':'Confirmed Cases'}, inplace = True)
Colombia.rename(columns = {'Colombia_y':'Death Cases'}, inplace = True)
Colombia["2020-04-03":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Colombia')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Czechia = pd.merge(confirmed_country["Czechia"], death["Czechia"], left_index=True, right_index=True)
Czechia.rename(columns = {'Czechia_x':'Confirmed Cases'}, inplace = True)
Czechia.rename(columns = {'Czechia_y':'Death Cases'}, inplace = True)
Czechia["2020-03-31":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Czechia')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Denmark = pd.merge(confirmed_country["Denmark"], death["Denmark"], left_index=True, right_index=True)
Denmark.rename(columns = {'Denmark_x':'Confirmed Cases'}, inplace = True)
Denmark.rename(columns = {'Denmark_y':'Death Cases'}, inplace = True)
Denmark["2020-03-24":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Denmark')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

dominicanrepublic = pd.merge(confirmed_country["Dominican Republic"], death["Dominican Republic"], left_index=True, right_index=True)
dominicanrepublic.rename(columns = {'Dominican Republic_x':'Confirmed Cases'}, inplace = True)
dominicanrepublic.rename(columns = {'Dominican Republic_y':'Death Cases'}, inplace = True)
dominicanrepublic["2020-03-28":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Dominican Republic')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Ecuador = pd.merge(confirmed_country["Ecuador"], death["Ecuador"], left_index=True, right_index=True)
Ecuador.rename(columns = {'Ecuador_x':'Confirmed Cases'}, inplace = True)
Ecuador.rename(columns = {'Ecuador_y':'Death Cases'}, inplace = True)
Ecuador["2020-03-24":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Ecuador')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Egypt = pd.merge(confirmed_country["Egypt"], death["Egypt"], left_index=True, right_index=True)
Egypt.rename(columns = {'Egypt_x':'Confirmed Cases'}, inplace = True)
Egypt.rename(columns = {'Egypt_y':'Death Cases'}, inplace = True)
Egypt["2020-03-27":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Egypt')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Finland = pd.merge(confirmed_country["Finland"], death["Finland"], left_index=True, right_index=True)
Finland.rename(columns = {'Finland_x':'Confirmed Cases'}, inplace = True)
Finland.rename(columns = {'Finland_y':'Death Cases'}, inplace = True)
Finland["2020-04-04":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Finland')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

France = pd.merge(confirmed_country["France"], death["France"], left_index=True, right_index=True)
France.rename(columns = {'France_x':'Confirmed Cases'}, inplace = True)
France.rename(columns = {'France_y':'Death Cases'}, inplace = True)
France["2020-03-10":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di France')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Germany = pd.merge(confirmed_country["Germany"], death["Germany"], left_index=True, right_index=True)
Germany.rename(columns = {'Germany_x':'Confirmed Cases'}, inplace = True)
Germany.rename(columns = {'Germany_y':'Death Cases'}, inplace = True)
Germany["2020-03-18":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Germany')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Greece = pd.merge(confirmed_country["Greece"], death["Greece"], left_index=True, right_index=True)
Greece.rename(columns = {'Greece_x':'Confirmed Cases'}, inplace = True)
Greece.rename(columns = {'Greece_y':'Death Cases'}, inplace = True)
Greece["2020-03-26":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Greece')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Hungary = pd.merge(confirmed_country["Hungary"], death["Hungary"], left_index=True, right_index=True)
Hungary.rename(columns = {'Hungary_x':'Confirmed Cases'}, inplace = True)
Hungary.rename(columns = {'Hungary_y':'Death Cases'}, inplace = True)
Hungary["2020-04-03":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Hungary')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

India = pd.merge(confirmed_country["India"], death["India"], left_index=True, right_index=True)
India.rename(columns = {'India_x':'Confirmed Cases'}, inplace = True)
India.rename(columns = {'India_y':'Death Cases'}, inplace = True)
India["2020-03-29":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di India')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Indonesia = pd.merge(confirmed_country["Indonesia"], death["Indonesia"], left_index=True, right_index=True)
Indonesia.rename(columns = {'Indonesia_x':'Confirmed Cases'}, inplace = True)
Indonesia.rename(columns = {'Indonesia_y':'Death Cases'}, inplace = True)
Indonesia["2020-03-19":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Indonesia')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Iran = pd.merge(confirmed_country["Iran"], death["Iran"], left_index=True, right_index=True)
Iran.rename(columns = {'Iran_x':'Confirmed Cases'}, inplace = True)
Iran.rename(columns = {'Iran_y':'Death Cases'}, inplace = True)
Iran["2020-02-27":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Iran')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Iraq = pd.merge(confirmed_country["Iraq"], death["Iraq"], left_index=True, right_index=True)
Iraq.rename(columns = {'Iraq_x':'Confirmed Cases'}, inplace = True)
Iraq.rename(columns = {'Iraq_y':'Death Cases'}, inplace = True)
Iraq["2020-03-24":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Iraq')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Ireland = pd.merge(confirmed_country["Ireland"], death["Ireland"], left_index=True, right_index=True)
Ireland.rename(columns = {'Ireland_x':'Confirmed Cases'}, inplace = True)
Ireland.rename(columns = {'Ireland_y':'Death Cases'}, inplace = True)
Ireland["2020-03-28":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Ireland')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Israel = pd.merge(confirmed_country["Israel"], death["Israel"], left_index=True, right_index=True)
Israel.rename(columns = {'Israel_x':'Confirmed Cases'}, inplace = True)
Israel.rename(columns = {'Israel_y':'Death Cases'}, inplace = True)
Israel["2020-04-01":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Israel')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Italy = pd.merge(confirmed_country["Italy"], death["Italy"], left_index=True, right_index=True)
Italy.rename(columns = {'Italy_x':'Confirmed Cases'}, inplace = True)
Italy.rename(columns = {'Italy_y':'Death Cases'}, inplace = True)
Italy["2020-02-29":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Italy')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Japan = pd.merge(confirmed_country["Japan"], death["Japan"], left_index=True, right_index=True)
Japan.rename(columns = {'Japan_x':'Confirmed Cases'}, inplace = True)
Japan.rename(columns = {'Japan_y':'Death Cases'}, inplace = True)
Japan["2020-03-16":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Japan')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

KoreaSouth = pd.merge(confirmed_country["Korea, South"], death["Korea, South"], left_index=True, right_index=True)
KoreaSouth.rename(columns = {'Korea, South_x':'Confirmed Cases'}, inplace = True)
KoreaSouth.rename(columns = {'Korea, South_y':'Death Cases'}, inplace = True)
KoreaSouth["2020-03-02":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Korea, South')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Luxembourg = pd.merge(confirmed_country["Luxembourg"], death["Luxembourg"], left_index=True, right_index=True)
Luxembourg.rename(columns = {'Luxembourg_x':'Confirmed Cases'}, inplace = True)
Luxembourg.rename(columns = {'Luxembourg_y':'Death Cases'}, inplace = True)
Luxembourg["2020-04-01":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Luxembourg')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Malaysia = pd.merge(confirmed_country["Malaysia"], death["Malaysia"], left_index=True, right_index=True)
Malaysia.rename(columns = {'Malaysia_x':'Confirmed Cases'}, inplace = True)
Malaysia.rename(columns = {'Malaysia_y':'Death Cases'}, inplace = True)
Malaysia["2020-03-27":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Malaysia')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Mexico = pd.merge(confirmed_country["Mexico"], death["Mexico"], left_index=True, right_index=True)
Mexico.rename(columns = {'Mexico_x':'Confirmed Cases'}, inplace = True)
Mexico.rename(columns = {'Mexico_y':'Death Cases'}, inplace = True)
Mexico["2020-03-31":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Mexico')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Morocco = pd.merge(confirmed_country["Morocco"], death["Morocco"], left_index=True, right_index=True)
Morocco.rename(columns = {'Morocco_x':'Confirmed Cases'}, inplace = True)
Morocco.rename(columns = {'Morocco_y':'Death Cases'}, inplace = True)
Morocco["2020-03-28":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Morocco')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
       
Netherlands = pd.merge(confirmed_country["Netherlands"], death["Netherlands"], left_index=True, right_index=True)
Netherlands.rename(columns = {'Netherlands_x':'Confirmed Cases'}, inplace = True)
Netherlands.rename(columns = {'Netherlands_y':'Death Cases'}, inplace = True)
Netherlands["2020-03-17":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Netherlands')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

NorthMacedonia = pd.merge(confirmed_country["North Macedonia"], death["North Macedonia"], left_index=True, right_index=True)
NorthMacedonia.rename(columns = {'North Macedonia_x':'Confirmed Cases'}, inplace = True)
NorthMacedonia.rename(columns = {'North Macedonia_y':'Death Cases'}, inplace = True)
NorthMacedonia["2020-04-07":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di North Macedonia')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Norway = pd.merge(confirmed_country["Norway"], death["Norway"], left_index=True, right_index=True)
Norway.rename(columns = {'Norway_x':'Confirmed Cases'}, inplace = True)
Norway.rename(columns = {'Norway_y':'Death Cases'}, inplace = True)
Norway["2020-03-29":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Norway')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Pakistan = pd.merge(confirmed_country["Pakistan"], death["Pakistan"], left_index=True, right_index=True)
Pakistan.rename(columns = {'Pakistan_x':'Confirmed Cases'}, inplace = True)
Pakistan.rename(columns = {'Pakistan_y':'Death Cases'}, inplace = True)
Pakistan["2020-03-31":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Pakistan')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Panama = pd.merge(confirmed_country["Panama"], death["Panama"], left_index=True, right_index=True)
Panama.rename(columns = {'Panama_x':'Confirmed Cases'}, inplace = True)
Panama.rename(columns = {'Panama_y':'Death Cases'}, inplace = True)
Panama["2020-03-31":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Panama')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Peru = pd.merge(confirmed_country["Peru"], death["Peru"], left_index=True, right_index=True)
Peru.rename(columns = {'Peru_x':'Confirmed Cases'}, inplace = True)
Peru.rename(columns = {'Peru_y':'Death Cases'}, inplace = True)
Peru["2020-03-31":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Peru')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Philippines = pd.merge(confirmed_country["Philippines"], death["Philippines"], left_index=True, right_index=True)
Philippines.rename(columns = {'Philippines_x':'Confirmed Cases'}, inplace = True)
Philippines.rename(columns = {'Philippines_y':'Death Cases'}, inplace = True)
Philippines["2020-03-22":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Philippines')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Poland = pd.merge(confirmed_country["Poland"], death["Poland"], left_index=True, right_index=True)
Poland.rename(columns = {'Poland_x':'Confirmed Cases'}, inplace = True)
Poland.rename(columns = {'Poland_y':'Death Cases'}, inplace = True)
Poland["2020-03-30":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Poland')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Portugal = pd.merge(confirmed_country["Portugal"], death["Portugal"], left_index=True, right_index=True)
Portugal.rename(columns = {'Portugal_x':'Confirmed Cases'}, inplace = True)
Portugal.rename(columns = {'Portugal_y':'Death Cases'}, inplace = True)
Portugal["2020-03-24":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Portugal')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Romania = pd.merge(confirmed_country["Romania"], death["Romania"], left_index=True, right_index=True)
Romania.rename(columns = {'Romania_x':'Confirmed Cases'}, inplace = True)
Romania.rename(columns = {'Romania_y':'Death Cases'}, inplace = True)
Romania["2020-03-27":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Romania')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Russia = pd.merge(confirmed_country["Russia"], deaths["Russia"], left_index=True, right_index=True)
Russia.rename(columns = {'Russia_x':'Confirmed Cases'}, inplace = True)
Russia.rename(columns = {'Russia_y':'Death Cases'}, inplace = True)
Russia["2020-04-02":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Russia')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)


SanMarino = pd.merge(confirmed_country["San Marino"], death["San Marino"], left_index=True, right_index=True)
SanMarino.rename(columns = {'San Marino_x':'Confirmed Cases'}, inplace = True)
SanMarino.rename(columns = {'San Marino_y':'Death Cases'}, inplace = True)
SanMarino["2020-03-30":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di San Marino')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

SaudiArabia = pd.merge(confirmed_country["Saudi Arabia"], death["Saudi Arabia"], left_index=True, right_index=True)
SaudiArabia.rename(columns = {'Saudi Arabia_x':'Confirmed Cases'}, inplace = True)
SaudiArabia.rename(columns = {'Saudi Arabia_y':'Death Cases'}, inplace = True)
SaudiArabia["2020-04-03":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Saudi Arabia')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Serbia = pd.merge(confirmed_country["Serbia"], death["Serbia"], left_index=True, right_index=True)
Serbia.rename(columns = {'Serbia_x':'Confirmed Cases'}, inplace = True)
Serbia.rename(columns = {'Serbia_y':'Death Cases'}, inplace = True)
Serbia["2020-04-01":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Serbia')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Slovenia = pd.merge(confirmed_country["Slovenia"], death["Slovenia"], left_index=True, right_index=True)
Slovenia.rename(columns = {'Slovenia_x':'Confirmed Cases'}, inplace = True)
Slovenia.rename(columns = {'Slovenia_y':'Death Cases'}, inplace = True)
Slovenia["2020-04-05":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Slovenia')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Spain = pd.merge(confirmed_country["Spain"], death["Spain"], left_index=True, right_index=True)
Spain.rename(columns = {'Spain_x':'Confirmed Cases'}, inplace = True)
Spain.rename(columns = {'Spain_y':'Death Cases'}, inplace = True)
Spain["2020-03-09":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Spain')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Sweden = pd.merge(confirmed_country["Sweden"], death["Sweden"], left_index=True, right_index=True)
Sweden.rename(columns = {'Sweden_x':'Confirmed Cases'}, inplace = True)
Sweden.rename(columns = {'Sweden_y':'Death Cases'}, inplace = True)
Sweden["2020-03-23":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Sweden')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Switzerland = pd.merge(confirmed_country["Switzerland"], death["Switzerland"], left_index=True, right_index=True)
Switzerland.rename(columns = {'Switzerland_x':'Confirmed Cases'}, inplace = True)
Switzerland.rename(columns = {'Switzerland_y':'Death Cases'}, inplace = True)
Switzerland["2020-03-17":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Switzerland')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Thailand = pd.merge(confirmed_country["Thailand"], death["Thailand"], left_index=True, right_index=True)
Thailand.rename(columns = {'Thailand_x':'Confirmed Cases'}, inplace = True)
Thailand.rename(columns = {'Thailand_y':'Death Cases'}, inplace = True)
Thailand["2020-04-06":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Thailand')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Turkey = pd.merge(confirmed_country["Turkey"], death["Turkey"], left_index=True, right_index=True)
Turkey.rename(columns = {'Turkey_x':'Confirmed Cases'}, inplace = True)
Turkey.rename(columns = {'Turkey_y':'Death Cases'}, inplace = True)
Turkey["2020-03-22":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Turkey')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

US = pd.merge(confirmed_country["US"], death["US"], left_index=True, right_index=True)
US.rename(columns = {'US_x':'Confirmed Cases'}, inplace = True)
US.rename(columns = {'US_y':'Death Cases'}, inplace = True)
US["2020-03-10":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di US')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

Ukraine = pd.merge(confirmed_country["Ukraine"], death["Ukraine"], left_index=True, right_index=True)
Ukraine.rename(columns = {'Ukraine_x':'Confirmed Cases'}, inplace = True)
Ukraine.rename(columns = {'Ukraine_y':'Death Cases'}, inplace = True)
Ukraine["2020-04-03":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Ukraine')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

UK = pd.merge(confirmed_country["United Kingdom"], death["United Kingdom"], left_index=True, right_index=True)
UK.rename(columns = {'United Kingdom_x':'Confirmed Cases'}, inplace = True)
UK.rename(columns = {'United Kingdom_y':'Death Cases'}, inplace = True)
UK["2020-03-16":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di United Kingdom')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)

C:\Users\BelindaM\anaconda3\lib\site-packages\pandas\plotting\_matplotlib\core.py:386: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  fig = self.plt.figure(figsize=self.figsize)


def data_kematian(raw_data_deaths):
    died = raw_data_deaths.melt(id_vars=['Province/State','Country/Region','Lat','Long'],
                                            value_vars=raw_data_deaths.iloc[:,4:],
                                            var_name='Date', value_name='Total Kasus Kematian')
   
    died["Date"] = pd.to_datetime(died["Date"])
    died = died.drop(["Province/State","Lat","Long"], axis = 1)
    died = died.groupby(['Date','Country/Region'],as_index=False).agg({'Total Kasus Kematian': 'sum'})
    return (died)

data_meninggal = data_kematian(raw_data_deaths)
list_nama = list(death.columns)
df=data_meninggal[data_meninggal['Country/Region'].isin(list_nama)]
df['Total Kasus Kematian'] = np.where((df['Total Kasus Kematian'] < 25 ) , 0, df['Total Kasus Kematian'])

C:\Users\BelindaM\AppData\Local\Temp\ipykernel_15068\2344823607.py:14: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df['Total Kasus Kematian'] = np.where((df['Total Kasus Kematian'] < 25 ) , 0, df['Total Kasus Kematian'])


fig = px.choropleth(df, locations="Country/Region", 
                    color=np.log(df["Total Kasus Kematian"]),
                    locationmode='country names', hover_name="Country/Region", 
                    animation_frame=df["Date"].dt.strftime('%Y-%m-%d'),
                    title='Total Kasus Kematian berdasarkan urutan waktu', color_continuous_scale=px.colors.sequential.matter)
fig.update(layout_coloraxis_showscale=False)
fig.show()

C:\Users\BelindaM\anaconda3\lib\site-packages\pandas\core\arraylike.py:397: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)


def setindex(recovered):
    recovered = recovered.set_index(['Date'])
    return recovered

def groupingsum(recovered):
    recovered = recovered.groupby(['Date','Country/Region'],as_index=False).agg({'Total Pasien Sembuh': 'sum'})
    recovered = recovered.pivot_table(index=['Date'], columns='Country/Region',
                     values='Total Pasien Sembuh', aggfunc='first').reset_index()
    return recovered


def buat_data_baru(raw_data_recovered):
    recovered = raw_data_recovered.melt(id_vars=['Province/State','Country/Region','Lat','Long'],
                                            value_vars=raw_data_recovered.iloc[:,4:],
                                            var_name='Date', value_name='Total Pasien Sembuh')
    recovered["Date"] = pd.to_datetime(recovered["Date"])
    recovered = recovered.drop(["Province/State","Lat","Long"], axis = 1)
    recovered = groupingsum(recovered)
    recovered = setindex(recovered)
    return (recovered)


recovery = buat_data_baru(raw_data_recovered)


recovery


copyrecovery=recovery.copy()
recovery["Total Pasien Sembuh dari COVID-19"] = recovery.sum(axis=1)


recovery.drop(recovery.iloc[:, 0:196], inplace = True, axis = 1)
sembuh = recovery['Total Pasien Sembuh dari COVID-19']
Sembuh = pd.DataFrame(sembuh)
Sembuh


plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
Sembuh["2020-01-22":"2022-01-11"].plot(linewidth=3);
plt.title('Kasus Sembuh COVID-19')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Sembuh dalam satuan ratusan juta')
plt.gcf().set_size_inches(12, 8)


def buat_data(raw_data_recovered):
    recovered2 = raw_data_recovered.melt(id_vars=['Province/State','Country/Region','Lat','Long'],
                                            value_vars=raw_data_recovered.iloc[:,4:],
                                            var_name='Date', value_name='Total Pasien Sembuh')
   
    recovered2["Date"] = pd.to_datetime(recovered2["Date"])
    recovered2 = recovered2.drop(["Province/State","Lat","Long"], axis = 1)
    recovered2 = recovered2.groupby(['Date','Country/Region'],as_index=False).agg({'Total Pasien Sembuh': 'sum'})
    return (recovered2)

recovery2 = buat_data(raw_data_recovered)
copy_recovery2=recovery2.copy()

recovery2 = recovery2[recovery2["Date"]=="2021-08-04"]

pd.set_option("mode.chained_assignment", None)
fig=px.choropleth(recovery2, locations="Country/Region", locationmode='country names', 
                  color_continuous_scale='dense', color=np.log10(recovery2["Total Pasien Sembuh"]), range_color=(0,10))
fig.show()

C:\Users\BelindaM\anaconda3\lib\site-packages\pandas\core\arraylike.py:397: RuntimeWarning:

divide by zero encountered in log10


def buat_dataa(raw_data_recovered):
    recovered3 = raw_data_recovered.melt(id_vars=['Province/State','Country/Region','Lat','Long'],
                                            value_vars=raw_data_recovered.iloc[:,4:],
                                            var_name='Date', value_name='Total Pasien Sembuh')
   
    recovered3["Date"] = pd.to_datetime(recovered3["Date"])
    recovered3 = recovered3.drop(["Province/State","Lat","Long"], axis = 1)
    recovered3 = recovered3.groupby(['Date','Country/Region'],as_index=False).agg({'Total Pasien Sembuh': 'sum'})
    return (recovered3)

recovery3 = buat_dataa(raw_data_recovered)

fig = px.choropleth(recovery3, locations="Country/Region", 
                    color=np.log(recovery3["Total Pasien Sembuh"]),
                    locationmode='country names', hover_name="Country/Region", 
                    animation_frame=recovery3["Date"].dt.strftime('%Y-%m-%d'),
                    title='Total Kasus Sembuh berdasarkan urutan waktu', color_continuous_scale=px.colors.sequential.matter)
fig.update(layout_coloraxis_showscale=False)
fig.show()

C:\Users\BelindaM\anaconda3\lib\site-packages\pandas\core\arraylike.py:397: RuntimeWarning:

divide by zero encountered in log


enamnegaraa = copyrecovery[["France", "Spain", "China", "US", "Italy", "Australia"]]
enamnegaraa = pd.DataFrame(enamnegaraa)
enamnegaraa
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
fig, axes = plt.subplots(nrows=6, ncols=1)
for i, c in enumerate(enamnegaraa.columns):
    enamnegaraa[c].plot(kind='line', ax=axes[i], title=c, xlabel="Tanggal", ylabel="Total Kasus Sembuh", legend=c)
plt.subplots_adjust(hspace = 1.0)
plt.gcf().set_size_inches(10, 28)


plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
enamnegaraa.plot(linewidth=3)
plt.title('Kasus Sembuh COVID-19 di Enam Negara dalam satuan juta')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Sembuh dalam satuan juta')
plt.gcf().set_size_inches(12, 8)

	Province/State	Country/Region	Lat	Long	1/26/20	1/27/20	...	3/29/20	3/30/20	3/31/20	4/1/20	4/2/20	4/3/20	4/4/20	4/5/20	4/6/20	4/7/20
0	NaN	Afghanistan	33.0000	65.0000	0	0	...	120	170	174	237	273	281	299	349	367	423
1	NaN	Albania	41.1533	20.1683	0	0	...	212	223	243	259	277	304	333	361	377	383
2	NaN	Algeria	28.0339	1.6596	0	0	...	511	584	716	847	986	1171	1251	1320	1423	1468
3	NaN	Andorra	42.5063	1.5218	0	0	...	334	370	376	390	428	439	466	501	525	545
4	NaN	Angola	-11.2027	17.8739	0	0	...	7	7	7	8	8	8	10	14	16	17
5	NaN	Antigua and Barbuda	17.0608	-61.7964	0	0	...	7	7	7	7	9	15	15	15	15	19
6	NaN	Argentina	-38.4161	-63.6167	0	0	...	745	820	1054	1054	1133	1265	1451	1451	1554	1628
7	NaN	Armenia	40.0691	45.0382	0	0	...	424	482	532	571	663	736	770	822	833	853
8	Australian Capital Territory	Australia	-35.4735	149.0124	0	0	...	77	78	80	84	87	91	93	96	96	96
9	New South Wales	Australia	-33.8688	151.2093	3	4	...	1791	2032	2032	2182	2298	2389	2493	2580	2637	2686

	Lat	Long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	1/28/20	1/29/20	...	3/29/20	3/30/20	3/31/20	4/1/20	4/2/20	4/3/20	4/4/20	4/5/20	4/6/20	4/7/20
count	263.000000	263.000000	263.000000	263.000000	263.000000	263.000000	263.000000	263.000000	263.000000	263.000000	...	263.000000	263.000000	263.000000	263.000000	263.000000	263.000000	263.000000	263.000000	263.000000	263.000000
mean	21.339244	22.068133	2.110266	2.486692	3.577947	5.452471	8.053232	11.129278	21.209125	23.444867	...	2738.174905	2974.885932	3260.406844	3546.026616	3852.927757	4166.984791	4552.870722	4836.939163	5114.452471	5422.418251
std	24.779585	70.785949	27.434015	27.532888	34.275498	47.702207	66.662110	89.815834	220.427512	221.769901	...	13348.022358	14659.339365	16274.718201	17892.269613	19740.409389	21707.026686	23983.928488	25717.561274	27517.452168	29418.401918
min	-51.796300	-135.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	6.938500	-21.031300	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	12.000000	14.000000	15.000000	17.000000	19.500000	20.500000	21.000000	22.000000	24.000000	27.000000
50%	23.634500	20.168300	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	126.000000	139.000000	143.000000	168.000000	176.000000	184.000000	195.000000	214.000000	226.000000	237.000000
75%	41.178850	79.500000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	642.500000	685.500000	715.000000	780.000000	881.000000	949.000000	983.500000	1020.000000	1068.500000	1135.500000
max	71.706900	178.065000	444.000000	444.000000	549.000000	761.000000	1058.000000	1423.000000	3554.000000	3554.000000	...	140909.000000	161837.000000	188172.000000	213372.000000	243616.000000	275586.000000	308850.000000	337072.000000	366667.000000	396223.000000

	Province/State	Country/Region	Lat	Long	Date
0	NaN	Afghanistan	33.0000	65.0000	1/22/20
1	NaN	Albania	41.1533	20.1683	1/22/20
2	NaN	Algeria	28.0339	1.6596	1/22/20
3	NaN	Andorra	42.5063	1.5218	1/22/20
4	NaN	Angola	-11.2027	17.8739	1/22/20
5	NaN	Antigua and Barbuda	17.0608	-61.7964	1/22/20
6	NaN	Argentina	-38.4161	-63.6167	1/22/20
7	NaN	Armenia	40.0691	45.0382	1/22/20
8	Australian Capital Territory	Australia	-35.4735	149.0124	1/22/20
9	New South Wales	Australia	-33.8688	151.2093	1/22/20

	Province/State	Country/Region	Lat	Long	Date	Total Pasien Terkonfirmasi
0	NaN	Afghanistan	33.00000	65.000000	2020-01-22	0
1	NaN	Albania	41.15330	20.168300	2020-01-22	0
2	NaN	Algeria	28.03390	1.659600	2020-01-22	0
3	NaN	Andorra	42.50630	1.521800	2020-01-22	0
4	NaN	Angola	-11.20270	17.873900	2020-01-22	0
...	...	...	...	...	...	...
20246	Falkland Islands (Malvinas)	United Kingdom	-51.79630	-59.523600	2020-04-07	2
20247	Saint Pierre and Miquelon	France	46.88520	-56.315900	2020-04-07	1
20248	NaN	South Sudan	6.87700	31.307000	2020-04-07	2
20249	NaN	Western Sahara	24.21550	-12.885800	2020-04-07	4
20250	NaN	Sao Tome and Principe	0.18636	6.613081	2020-04-07	4

Country/Region	Date	Afghanistan	Albania	Algeria	Andorra	Angola	Antigua and Barbuda	Argentina	Armenia	Australia	...	United Arab Emirates	United Kingdom	Uruguay	Uzbekistan	Venezuela	Vietnam	West Bank and Gaza	Western Sahara	Zambia	Zimbabwe
0	2020-01-22	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
1	2020-01-23	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	2	0	0	0	0
2	2020-01-24	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	2	0	0	0	0
3	2020-01-25	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	2	0	0	0	0
4	2020-01-26	0	0	0	0	0	0	0	0	4	...	0	0	0	0	0	2	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
72	2020-04-03	281	304	1171	439	8	15	1265	736	5330	...	1264	38689	369	227	153	237	194	0	39	9
73	2020-04-04	299	333	1251	466	10	15	1451	770	5550	...	1505	42477	400	266	155	240	217	0	39	9
74	2020-04-05	349	361	1320	501	14	15	1451	822	5687	...	1799	48436	400	342	159	241	237	4	39	9
75	2020-04-06	367	377	1423	525	16	15	1554	833	5797	...	2076	52279	406	457	165	245	254	4	39	10
76	2020-04-07	423	383	1468	545	17	19	1628	853	5895	...	2359	55949	424	520	165	249	261	4	39	11

UAS - INTRODUCTION TO DATA SCIENCE - STAT6152016¶

Format nama files: nim_nama.ipynb; nim_nama.pdf; nim_nama.rar¶

NIM: 2540119596¶

Nama: Belinda Mutiara¶

Exploratory data analysis and visualization using Python¶

Memuat Libraries¶

Memuat Data¶

Kasus terkonfirmasi COVID-19¶

1. Jumlah kasus terkonfirmasi COVID-19 berdasarkan negara (Bobot: 15%)¶

2. Visualisasi kasus terkonfirmasi COVID-19 berdasarkan negara (Bobot: 15%)¶

Tuliskan analisis anda berdasarkan hasil visualisasi diatas:¶

3. Jumlah kasus kematian COVID-19 yang dilaporkan berdasarkan negara (Bobot: 15%)¶

4. Penyelarasan kurva pertumbuhan seluruh negara dengan jumlah kasus kematian COVID-19 ≥ 25 (Bobot: 25%)¶

5. Visualisasi jumlah pasien yang sembuh (recovered people) (Bobot: 25%)¶

Country/Region	Afghanistan	Albania	Algeria	Andorra	Angola	Antigua and Barbuda	Argentina	Armenia	Australia	Austria	Azerbaijan	Bahamas	Bahrain	Bangladesh	Barbados	Belarus	Belgium	Belize	Benin	Bhutan	Bolivia	Bosnia and Herzegovina	Botswana	Brazil	Brunei	Bulgaria	Burkina Faso	Burma	Burundi	Cabo Verde	Cambodia	Cameroon	Canada	Central African Republic	Chad	Chile	China	Colombia	Congo (Brazzaville)	Congo (Kinshasa)	Costa Rica	Cote d'Ivoire	Croatia	Cuba	Cyprus	Czechia	Denmark	Diamond Princess	Djibouti	Dominica	Dominican Republic	Ecuador	Egypt	El Salvador	Equatorial Guinea	Eritrea	Estonia	Eswatini	Ethiopia	Fiji	Finland	France	Gabon	Gambia	Georgia	Germany	Ghana	Greece	Grenada	Guatemala	Guinea	Guinea-Bissau	Guyana	Haiti	Holy See	Honduras	Hungary	Iceland	India	Indonesia	Iran	Iraq	Ireland	Israel	Italy	Jamaica	Japan	Jordan	Kazakhstan	Kenya	Korea, South	Kosovo	Kuwait	Kyrgyzstan	Laos	Latvia	Lebanon	Liberia	Libya	Liechtenstein	Lithuania	Luxembourg	MS Zaandam	Madagascar	Malawi	Malaysia	Maldives	Mali	Malta	Mauritania	Mauritius	Mexico	Moldova	Monaco	Mongolia	Montenegro	Morocco	Mozambique	Namibia	Nepal	Netherlands	New Zealand	Nicaragua	Niger	Nigeria	North Macedonia	Norway	Oman	Pakistan	Panama	Papua New Guinea	Paraguay	Peru	Philippines	Poland	Portugal	Qatar	Romania	Russia	Rwanda	Saint Kitts and Nevis	Saint Lucia	Saint Vincent and the Grenadines	San Marino	Sao Tome and Principe	Saudi Arabia	Senegal	Serbia	Seychelles	Sierra Leone	Singapore	Slovakia	Slovenia	Somalia	South Africa	South Sudan	Spain	Sri Lanka	Sudan	Suriname	Sweden	Switzerland	Syria	Taiwan*	Tanzania	Thailand	Timor-Leste	Togo	Trinidad and Tobago	Tunisia	Turkey	US	Uganda	Ukraine	United Arab Emirates	United Kingdom	Uruguay	Uzbekistan	Venezuela	Vietnam	West Bank and Gaza	Western Sahara	Zambia	Zimbabwe
count	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000	77.000000
mean	44.857143	58.779221	172.649351	78.974026	1.675325	2.025974	208.636364	121.389610	966.038961	2168.220779	72.636364	3.714286	165.857143	15.090909	8.012987	62.961039	2808.402597	0.662338	2.597403	1.038961	22.402597	92.376623	0.532468	1412.376623	34.103896	92.207792	54.337662	2.545455	0.285714	1.233766	26.610390	60.207792	1931.311688	1.129870	1.233766	602.571429	61878.272727	206.168831	4.116883	21.662338	78.090909	36.766234	185.545455	40.103896	64.831169	731.675325	811.779221	480.883117	6.961039	2.246753	224.792208	524.064935	192.207792	7.012987	3.116883	3.168831	186.285714	1.818182	6.142857	1.545455	341.441558	12822.610390	3.142857	0.818182	30.987013	16231.623377	34.675325	324.363636	1.701299	9.584416	9.909091	1.896104	4.064935	3.064935	1.376623	33.363636	106.714286	276.025974	444.220779	350.883117	11894.363636	164.688312	721.415584	1136.311688	26992.792208	9.766234	669.610390	60.753247	73.883117	16.636364	4259.935065	19.727273	101.870130	21.805195	1.649351	88.389610	114.402597	1.441558	1.818182	15.857143	115.012987	467.194805	0.922078	9.987013	0.350649	681.311688	5.480519	5.207792	42.428571	1.142857	29.766234	249.207792	97.194805	11.792208	3.246753	26.389610	131.883117	1.636364	2.610390	1.883117	2852.441558	133.337662	0.922078	16.493506	30.532468	75.948052	1169.337662	47.948052	475.168831	244.831169	0.272727	15.740260	280.207792	431.961039	546.714286	1559.272727	259.350649	507.454545	606.727273	16.233766	1.181818	2.090909	0.688312	68.311688	0.103896	362.220779	38.181818	230.532468	2.376623	0.376623	281.051948	88.142857	199.155844	0.974026	275.259740	0.051948	20967.662338	34.623377	1.636364	2.051948	1152.454545	3855.233766	2.168831	91.272727	4.064935	374.597403	0.220779	7.805195	18.935065	80.454545	2971.948052	41931.961039	7.168831	144.688312	216.051948	6072.844156	70.350649	42.337662	30.935065	59.649351	36.532468	0.155844	6.025974	1.532468
std	96.349516	105.241405	366.068974	154.181538	3.729123	4.301083	427.103461	235.506629	1787.144278	3922.499588	159.561151	8.063657	229.753169	29.775114	16.413644	154.117474	5673.749942	1.535633	5.788509	1.584809	46.622295	189.210760	1.518174	3127.909357	51.186600	164.768285	106.381712	6.309027	0.856202	2.333374	42.747778	155.541443	4233.173254	2.154253	2.655164	1273.367587	29056.328977	430.525517	10.131989	45.138270	142.085888	81.235022	358.443035	91.019313	127.137659	1386.498528	1353.824637	307.498779	18.386524	4.718820	494.007760	1064.491296	344.350355	17.088387	5.443427	7.834476	323.878505	3.370644	12.059845	3.354280	600.881797	25646.732643	6.744811	1.466533	50.706714	30258.667724	71.350219	537.939977	3.755925	18.587879	29.666205	5.524073	7.788888	6.471268	2.322878	75.863088	211.377615	467.988584	1048.533294	680.517546	17903.433811	283.762422	1449.580296	2422.764734	42182.079927	16.860805	922.346367	110.526272	165.468472	39.069408	4216.801842	45.489917	156.118237	49.664410	3.644629	163.182748	179.527396	3.109804	4.781343	26.753118	236.738669	885.460630	2.609528	22.308538	1.305517	1140.920384	7.284826	13.047825	74.099760	2.030778	65.166567	542.709512	226.408750	21.981055	5.226843	58.882612	284.983378	3.410159	4.801743	2.139688	5355.346987	294.984505	1.797412	52.135104	65.877395	151.462597	1856.982730	87.639948	945.675398	516.622009	0.503576	30.247402	614.361272	939.844408	1143.532383	3255.235611	440.944553	1075.276030	1508.093591	31.380473	2.932249	4.249402	1.648442	94.647443	0.640403	707.483766	69.206732	524.175031	3.762834	1.287991	377.252808	155.031806	322.872228	2.025976	534.143964	0.276067	39780.792713	56.595062	3.248113	3.586877	2000.654844	6749.319301	5.094896	116.505211	7.354443	655.328709	0.417492	15.066553	34.769291	166.073188	7446.266028	93967.244676	15.737487	349.981814	474.462075	13196.987195	129.867520	100.384997	53.837557	76.847896	63.904448	0.779084	12.890198	3.076158
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	548.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	2.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	2.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	15.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	7.000000	0.000000	0.000000	0.000000	42354.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	135.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	11.000000	0.000000	0.000000	0.000000	14.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.000000	0.000000	26.000000	0.000000	0.000000	0.000000	27.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	18.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.000000	0.000000	0.000000	0.000000	0.000000	2.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	45.000000	0.000000	0.000000	0.000000	0.000000	0.000000	2.000000	1.000000	0.000000	0.000000	1.000000	0.000000	0.000000	18.000000	0.000000	32.000000	0.000000	0.000000	0.000000	0.000000	0.000000	11.000000	0.000000	0.000000	8.000000	8.000000	0.000000	0.000000	0.000000	14.000000	0.000000	0.000000	0.000000	0.000000
50%	1.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	25.000000	9.000000	0.000000	0.000000	41.000000	0.000000	0.000000	1.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	2.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	20.000000	0.000000	0.000000	0.000000	79356.000000	0.000000	0.000000	0.000000	0.000000	0.000000	6.000000	0.000000	0.000000	0.000000	3.000000	705.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	3.000000	100.000000	0.000000	0.000000	1.000000	79.000000	0.000000	4.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	3.000000	0.000000	593.000000	13.000000	1.000000	7.000000	1128.000000	0.000000	241.000000	0.000000	0.000000	0.000000	3150.000000	0.000000	45.000000	0.000000	0.000000	0.000000	4.000000	0.000000	0.000000	0.000000	1.000000	1.000000	0.000000	0.000000	0.000000	25.000000	0.000000	0.000000	0.000000	0.000000	0.000000	4.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	6.000000	1.000000	0.000000	0.000000	1.000000	1.000000	15.000000	6.000000	4.000000	0.000000	0.000000	0.000000	0.000000	3.000000	0.000000	0.000000	1.000000	3.000000	2.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	102.000000	0.000000	0.000000	0.000000	0.000000	0.000000	45.000000	1.000000	0.000000	0.000000	12.000000	18.000000	0.000000	39.000000	0.000000	42.000000	0.000000	0.000000	0.000000	0.000000	0.000000	68.000000	0.000000	0.000000	21.000000	23.000000	0.000000	0.000000	0.000000	16.000000	0.000000	0.000000	0.000000	0.000000
75%	22.000000	64.000000	87.000000	53.000000	0.000000	1.000000	97.000000	115.000000	681.000000	2013.000000	44.000000	3.000000	278.000000	17.000000	5.000000	51.000000	1795.000000	0.000000	2.000000	1.000000	12.000000	63.000000	0.000000	621.000000	75.000000	94.000000	33.000000	0.000000	0.000000	0.000000	37.000000	13.000000	800.000000	1.000000	1.000000	238.000000	81156.000000	102.000000	3.000000	14.000000	69.000000	9.000000	105.000000	11.000000	67.000000	694.000000	1225.000000	712.000000	1.000000	0.000000	34.000000	199.000000	256.000000	1.000000	6.000000	0.000000	267.000000	1.000000	6.000000	1.000000	400.000000	10970.000000	1.000000	1.000000	40.000000	15320.000000	11.000000	418.000000	0.000000	9.000000	1.000000	0.000000	5.000000	0.000000	1.000000	12.000000	73.000000	330.000000	194.000000	311.000000	18407.000000	192.000000	557.000000	427.000000	41035.000000	15.000000	924.000000	69.000000	44.000000	7.000000	8565.000000	0.000000	148.000000	3.000000	0.000000	86.000000	157.000000	2.000000	0.000000	28.000000	36.000000	335.000000	0.000000	0.000000	0.000000	900.000000	13.000000	0.000000	53.000000	2.000000	3.000000	118.000000	49.000000	7.000000	6.000000	3.000000	63.000000	0.000000	3.000000	1.000000	2467.000000	28.000000	1.000000	0.000000	8.000000	48.000000	1746.000000	48.000000	454.000000	109.000000	0.000000	11.000000	234.000000	217.000000	355.000000	785.000000	460.000000	277.000000	199.000000	8.000000	0.000000	2.000000	1.000000	119.000000	0.000000	274.000000	31.000000	103.000000	6.000000	0.000000	345.000000	123.000000	286.000000	1.000000	150.000000	0.000000	17963.000000	60.000000	2.000000	1.000000	1439.000000	4075.000000	0.000000	108.000000	6.000000	272.000000	0.000000	1.000000	9.000000	39.000000	192.000000	13747.000000	0.000000	16.000000	140.000000	2716.000000	79.000000	23.000000	42.000000	85.000000	44.000000	0.000000	2.000000	0.000000
max	423.000000	383.000000	1468.000000	545.000000	17.000000	19.000000	1628.000000	853.000000	5895.000000	12639.000000	717.000000	33.000000	811.000000	164.000000	63.000000	861.000000	22194.000000	7.000000	26.000000	5.000000	194.000000	764.000000	6.000000	14034.000000	135.000000	577.000000	384.000000	22.000000	3.000000	7.000000	115.000000	658.000000	17872.000000	8.000000	10.000000	5116.000000	82718.000000	1780.000000	45.000000	180.000000	483.000000	349.000000	1282.000000	396.000000	494.000000	5017.000000	5266.000000	712.000000	90.000000	15.000000	1956.000000	3747.000000	1450.000000	78.000000	16.000000	31.000000	1149.000000	10.000000	52.000000	15.000000	2308.000000	110065.000000	30.000000	4.000000	196.000000	107663.000000	287.000000	1832.000000	12.000000	77.000000	144.000000	33.000000	33.000000	25.000000	7.000000	305.000000	817.000000	1586.000000	5311.000000	2738.000000	62589.000000	1122.000000	5709.000000	9248.000000	135586.000000	63.000000	3906.000000	353.000000	697.000000	172.000000	10331.000000	170.000000	743.000000	228.000000	14.000000	548.000000	548.000000	14.000000	20.000000	78.000000	880.000000	2970.000000	9.000000	88.000000	8.000000	3963.000000	19.000000	56.000000	293.000000	6.000000	268.000000	2439.000000	1056.000000	79.000000	15.000000	241.000000	1184.000000	10.000000	16.000000	9.000000	19709.000000	1160.000000	6.000000	278.000000	254.000000	599.000000	6086.000000	371.000000	4035.000000	2100.000000	2.000000	115.000000	2954.000000	3764.000000	4848.000000	12442.000000	2057.000000	4417.000000	7497.000000	105.000000	11.000000	14.000000	8.000000	279.000000	4.000000	2795.000000	237.000000	2447.000000	11.000000	6.000000	1481.000000	581.000000	1059.000000	8.000000	1749.000000	2.000000	141942.000000	185.000000	14.000000	10.000000	7693.000000	22253.000000	19.000000	376.000000	24.000000	2258.000000	1.000000	65.000000	107.000000	623.000000	34109.000000	396223.000000	52.000000	1462.000000	2359.000000	55949.000000	424.000000	520.000000	165.000000	249.000000	261.000000	4.000000	39.000000	11.000000

Country/Region	Afghanistan	Albania	Algeria	Andorra	Angola	Antigua and Barbuda	Argentina	Armenia	Australia	Austria	Azerbaijan	Bahamas	Bahrain	Bangladesh	Barbados	Belarus	Belgium	Belize	Benin	Bhutan	Bolivia	Bosnia and Herzegovina	Botswana	Brazil	Brunei	Bulgaria	Burkina Faso	Burma	Burundi	Cabo Verde	Cambodia	Cameroon	Canada	Central African Republic	Chad	Chile	China	Colombia	Congo (Brazzaville)	Congo (Kinshasa)	Costa Rica	Cote d'Ivoire	Croatia	Cuba	Cyprus	Czechia	Denmark	Diamond Princess	Djibouti	Dominica	Dominican Republic	Ecuador	Egypt	El Salvador	Equatorial Guinea	Eritrea	Estonia	Eswatini	Ethiopia	Fiji	Finland	France	Gabon	Gambia	Georgia	Germany	Ghana	Greece	Grenada	Guatemala	Guinea	Guinea-Bissau	Guyana	Haiti	Holy See	Honduras	Hungary	Iceland	India	Indonesia	Iran	Iraq	Ireland	Israel	Italy	Jamaica	Japan	Jordan	Kazakhstan	Kenya	Korea, South	Kosovo	Kuwait	Kyrgyzstan	Laos	Latvia	Lebanon	Liberia	Libya	Liechtenstein	Lithuania	Luxembourg	MS Zaandam	Madagascar	Malawi	Malaysia	Maldives	Mali	Malta	Mauritania	Mauritius	Mexico	Moldova	Monaco	Mongolia	Montenegro	Morocco	Mozambique	Namibia	Nepal	Netherlands	New Zealand	Nicaragua	Niger	Nigeria	North Macedonia	Norway	Oman	Pakistan	Panama	Papua New Guinea	Paraguay	Peru	Philippines	Poland	Portugal	Qatar	Romania	Russia	Rwanda	Saint Kitts and Nevis	Saint Lucia	Saint Vincent and the Grenadines	San Marino	Sao Tome and Principe	Saudi Arabia	Senegal	Serbia	Seychelles	Sierra Leone	Singapore	Slovakia	Slovenia	Somalia	South Africa	South Sudan	Spain	Sri Lanka	Sudan	Suriname	Sweden	Switzerland	Syria	Taiwan*	Tanzania	Thailand	Timor-Leste	Togo	Trinidad and Tobago	Tunisia	Turkey	US	Uganda	Ukraine	United Arab Emirates	United Kingdom	Uruguay	Uzbekistan	Venezuela	Vietnam	West Bank and Gaza	Western Sahara	Zambia	Zimbabwe
Date
2020-04-03	6	17	105	16	2	0	39	7	28	168	5	1	4	6	0	4	1143	0	0	0	9	17	1	359	1	14	16	1	0	1	0	8	179	0	0	22	3326	25	2	13	2	1	8	6	11	53	139	11	0	0	68	145	66	2	0	0	12	0	0	0	20	6520	1	1	0	1275	5	63	0	1	0	0	4	0	0	15	26	4	72	181	3294	54	120	40	14681	3	63	5	6	4	174	1	0	1	0	1	17	0	1	0	9	31	2	0	0	53	0	3	0	1	7	50	8	1	0	2	48	0	0	0	1490	1	1	5	4	12	59	1	40	37	0	3	61	136	71	246	3	133	34	0	0	0	0	30	0	25	1	39	0	0	5	1	20	0	9	0	11198	4	2	1	358	591	2	5	1	19	0	3	6	18	425	7087	0	27	9	3611	4	2	7	0	1	0	1	1
2020-04-04	7	20	130	17	2	0	43	7	30	186	5	4	4	8	0	5	1283	0	0	0	10	21	1	445	1	17	16	1	0	1	0	9	218	0	0	27	3330	32	2	18	2	1	12	6	11	59	161	11	0	0	68	172	71	3	0	0	13	0	0	0	25	7574	1	1	1	1444	5	68	0	2	0	0	4	0	0	15	32	4	86	191	3452	56	137	44	15362	3	77	5	5	4	177	1	1	1	0	1	17	1	1	1	11	31	2	0	0	57	0	3	0	1	7	60	12	1	0	2	59	0	0	0	1656	1	1	8	4	17	62	2	41	41	0	3	73	144	79	266	3	146	43	0	0	0	0	32	0	29	2	44	0	0	6	1	22	0	9	0	11947	5	2	1	373	666	2	5	1	20	0	3	6	18	501	8407	0	32	10	4320	5	2	7	0	1	0	1	1
2020-04-05	7	20	152	18	2	0	44	7	35	204	7	4	4	9	1	8	1447	0	0	0	10	23	1	486	1	20	17	1	0	1	0	9	259	0	0	34	3333	35	5	18	2	3	15	8	9	67	179	11	0	0	82	180	78	3	0	0	15	0	2	0	28	8093	1	1	2	1584	5	73	0	2	0	0	4	1	0	22	34	4	99	198	3603	61	158	49	15887	3	77	5	6	4	183	1	1	1	0	1	18	3	1	1	13	36	2	0	0	61	0	5	0	1	7	79	15	1	0	2	70	0	0	0	1771	1	1	10	5	18	71	2	47	46	0	3	83	152	94	295	4	151	45	0	0	0	0	32	0	34	2	51	0	0	6	1	28	0	11	0	12641	5	2	1	401	715	2	5	1	23	0	3	7	22	574	9619	0	37	10	4943	5	2	7	0	1	0	1	1
2020-04-06	11	21	173	21	2	0	48	8	40	220	7	5	4	12	2	13	1632	1	1	0	11	29	1	564	1	22	18	1	0	1	0	9	339	0	0	37	3335	46	5	18	2	3	16	9	9	78	187	11	0	0	86	191	85	4	0	0	19	0	2	0	27	8926	1	1	2	1810	5	79	0	3	0	0	4	1	0	22	38	6	136	209	3739	64	174	57	16523	3	85	6	6	6	186	1	1	4	0	1	19	3	1	1	15	41	2	0	0	62	0	5	0	1	7	94	19	1	0	2	80	0	0	0	1874	1	1	10	5	23	76	2	53	54	0	5	92	163	107	311	4	176	47	0	0	0	0	32	0	38	2	58	0	0	6	2	30	0	12	0	13341	5	2	1	477	765	2	5	1	26	0	3	8	22	649	10783	0	38	11	5385	6	2	7	0	1	0	1	1
2020-04-07	14	22	193	22	2	1	56	8	45	243	8	6	5	17	3	13	2035	1	1	0	14	33	1	686	1	23	19	1	0	1	0	9	375	0	0	43	3335	50	5	18	2	3	18	11	9	88	203	11	0	0	98	191	94	4	0	0	21	0	2	0	34	10343	1	1	3	2016	5	81	0	3	0	0	5	1	0	22	47	6	150	221	3872	65	210	65	17127	3	92	6	6	6	192	4	1	4	0	2	19	3	1	1	15	44	2	0	1	63	0	5	0	1	7	125	22	1	0	2	90	0	0	0	2108	1	1	11	6	26	89	2	57	55	0	5	107	177	129	345	6	197	58	0	0	0	0	34	0	41	2	61	0	0	6	2	36	0	13	0	14045	6	2	1	591	821	2	5	1	27	0	3	8	23	725	12722	0	45	12	6171	7	2	7	0	1	0	1	2

	Algeria
Date
2020-03-26	25
2020-03-27	26
2020-03-28	29
2020-03-29	31
2020-03-30	35
2020-03-31	44
2020-04-01	58
2020-04-02	86
2020-04-03	105
2020-04-04	130
2020-04-05	152
2020-04-06	173
2020-04-07	193

UAS - INTRODUCTION TO DATA SCIENCE - STAT6152016¶

Format nama files: nim_nama.ipynb; nim_nama.pdf; nim_nama.rar¶

NIM: 2540119596¶

Nama: Belinda Mutiara¶

Link Video: https://drive.google.com/drive/folders/1wU5cNLLA4GwUzGYtCEwamIJ9Qu440wVm?usp=sharing¶

Exploratory data analysis and visualization using Python¶

Memuat Libraries¶

Memuat Data¶

Kasus terkonfirmasi COVID-19¶

1. Jumlah kasus terkonfirmasi COVID-19 berdasarkan negara (Bobot: 15%)¶

2. Visualisasi kasus terkonfirmasi COVID-19 berdasarkan negara (Bobot: 15%)¶

Tuliskan analisis anda berdasarkan hasil visualisasi diatas:¶

3. Jumlah kasus kematian COVID-19 yang dilaporkan berdasarkan negara (Bobot: 15%)¶

4. Penyelarasan kurva pertumbuhan seluruh negara dengan jumlah kasus kematian COVID-19 ≥ 25 (Bobot: 25%)¶

5. Visualisasi jumlah pasien yang sembuh (recovered people) (Bobot: 25%)¶

Link Video: https://drive.google.com/drive/folders/1wU5cNLLA4GwUzGYtCEwamIJ9Qu440wVm?usp=sharing ¶